CN115392565A - Low-carbon operation optimization method and device for multifunctional park - Google Patents
Low-carbon operation optimization method and device for multifunctional park Download PDFInfo
- Publication number
- CN115392565A CN115392565A CN202211022569.9A CN202211022569A CN115392565A CN 115392565 A CN115392565 A CN 115392565A CN 202211022569 A CN202211022569 A CN 202211022569A CN 115392565 A CN115392565 A CN 115392565A
- Authority
- CN
- China
- Prior art keywords
- carbon
- energy
- park
- power
- energy storage
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 229910052799 carbon Inorganic materials 0.000 title claims abstract description 204
- 238000000034 method Methods 0.000 title claims abstract description 21
- 238000005457 optimization Methods 0.000 title claims abstract description 14
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 claims abstract description 172
- 238000004146 energy storage Methods 0.000 claims abstract description 95
- 238000007599 discharging Methods 0.000 claims abstract description 34
- 238000010276 construction Methods 0.000 claims abstract description 26
- 238000010248 power generation Methods 0.000 claims abstract description 26
- 230000009467 reduction Effects 0.000 claims abstract description 18
- 238000010977 unit operation Methods 0.000 claims description 13
- 230000009194 climbing Effects 0.000 claims description 9
- 238000004364 calculation method Methods 0.000 claims description 6
- 230000006870 function Effects 0.000 description 12
- 238000006243 chemical reaction Methods 0.000 description 2
- 238000004590 computer program Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 230000004931 aggregating effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- GVVPGTZRZFNKDS-JXMROGBWSA-N geranyl diphosphate Chemical compound CC(C)=CCC\C(C)=C\CO[P@](O)(=O)OP(O)(O)=O GVVPGTZRZFNKDS-JXMROGBWSA-N 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000006386 neutralization reaction Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000010355 oscillation Effects 0.000 description 1
- 239000011541 reaction mixture Substances 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 230000017105 transposition Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
- G06Q30/0206—Price or cost determination based on market factors
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Development Economics (AREA)
- General Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Human Resources & Organizations (AREA)
- Finance (AREA)
- Marketing (AREA)
- Physics & Mathematics (AREA)
- Accounting & Taxation (AREA)
- General Physics & Mathematics (AREA)
- Game Theory and Decision Science (AREA)
- Tourism & Hospitality (AREA)
- Health & Medical Sciences (AREA)
- Data Mining & Analysis (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The invention discloses a method and a device for optimizing low-carbon operation of a multi-energy park. The low-carbon operation optimization method for the multi-energy park comprises the following steps: calculating the carbon potential of the power grid nodes of the multi-energy park according to the prediction curves of the electric quantity and the carbon flow input by the power grid to the multi-energy park; constructing an energy storage charging and discharging state judgment model according to the carbon potential of the power grid node; constructing an objective function according to the minimum sum of the power generation cost, the carbon emission cost and the energy storage operation cost, and solving various generator set operation curves and energy storage charge-discharge curves in the multi-energy park by combining the energy storage charge-discharge state judgment model and the structural constraint condition; and calculating the carbon emission reduction volume of the multi-energy park before and after the construction of the energy storage system according to the operation curves of the various generator sets and the energy storage charging and discharging curves. The invention can optimize the low-carbon operation of the multifunctional park and effectively reduce the carbon emission of the multifunctional park.
Description
Technical Field
The invention relates to the technical field of low-carbon operation of power systems, in particular to a method and a device for optimizing low-carbon operation of a multi-energy park.
Background
With the increasingly prominent energy problem and climate change problem, the realization of low-carbon development and the reduction of excessive consumption of fossil energy are common targets of human society. The low-carbon transformation in the power industry is considered as the key of low-carbon development and is also the core measure for achieving the goals of carbon peak reaching and carbon neutralization.
In recent years, carbon emission flow theory has brought a new direction for low carbon power. The carbon emission flow is regarded as a virtual network flow existing in analogy to the active power flow in the power grid, and is used for representing the carbon emission corresponding to the flowing power on a certain branch. After carbon emission flow is introduced, the real-time carbon potential of any node of the system can be obtained according to any known system tidal current distribution, and the distributed power supply is guided to regulate output force, so that the minimum carbon emission of the system is realized. Currently, the research related to the low-carbon multi-energy system based on the carbon emission flow mainly focuses on the macroscopic discussion and planning level, and relatively few researches on the optimization operation are carried out.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a method and a device for optimizing the low-carbon operation of a multifunctional park, which can optimize the low-carbon operation of the multifunctional park and effectively reduce the carbon emission of the multifunctional park.
In order to solve the above technical problem, in a first aspect, an embodiment of the present invention provides a method for optimizing low-carbon operation in a multi-energy park, including:
calculating the carbon potential of the power grid nodes of the multi-energy park according to the prediction curves of the electric quantity and the carbon flow input by the power grid to the multi-energy park;
constructing an energy storage charging and discharging state judgment model according to the carbon potential of the power grid node;
constructing an objective function according to the minimum sum of the power generation cost, the carbon emission cost and the energy storage operation cost, and solving various generator set operation curves and energy storage charge-discharge curves in the multi-energy park by combining the energy storage charge-discharge state judgment model and the structural constraint condition;
and calculating the carbon emission reduction amount of the multi-energy park before and after the construction of the energy storage system according to the operation curves of the various generator sets and the energy storage charging and discharging curves.
Further, the carbon potential of the grid node is as follows:
wherein e is k The carbon emission value of the equivalent power generation side caused by the consumption of unit electric quantity of the kth node of the multi-energy park; n is a radical of + The set of all branches into which carbon flows in the branch connected with the kth node; l is the branch number; r l The carbon flow rate of the l branch is,
f is the carbon flow of the first branch, t m Is a unit time; p l The injected power at the kth node for the lth branch.
Further, the energy storage charge-discharge state judgment model is as follows:
wherein M is in the order of 10 4 Positive integer of (1), y 1 、y 2 Are all variable quantities of 0/1, and the like,
for storing the discharge carbon potential at the time t, t is equal to 0]T is a predetermined period, e k,t The charging carbon potential of the kth node of the multi-energy park at the time t; when y is 1 ＝0、y 2 =1, and
when the stored energy is in a discharge state, when y 1 ＝1、y 2 =0, and
when the stored energy is in a charging state, when y 1 ＝y 2 =0, and
and meanwhile, the stored energy is in an off-grid state.
Further, the objective function is:
wherein T is a preset period, T belongs to [0]；
The unit operation costs of the v-th wind generating set, the m-th photovoltaic generating set and the n-th gas generating set in the multi-energy park respectively,
the power generation powers of the v-th wind generating set, the m-th photovoltaic generating set and the n-th gas generating set at the moment t are respectively; c. C emi In order to be a carbon tax, the carbon tax is,
the carbon potential of the nth gas generator set at the time t, C ESS For unit operating cost of stored energy, P t Storing the charging or discharging power at the moment t.
Further, the constraint conditions comprise line power flow constraint, unit operation constraint and multi-energy load balance constraint.
Further, the line power flow constraints include line power constraints and node voltage constraints;
the line power constraint is:
|P l |≤P lmax ，|Q l |≤Q lmax ；
wherein, P l Active power of the l branch, P lmax The maximum active power of the l branch; q l For reactive power of the l branch, Q lmax Is the largest branch of the lReactive power;
the node voltage constraint is as follows:
U k,min ≤U k ≤U k,max ；
wherein, U k Voltage at kth node of the multi-energy park, U k,min Is the minimum voltage of the kth node, U k,max Is the maximum voltage of the kth node.
Further, the unit operation constraint comprises a unit power constraint and a unit climbing constraint;
the unit power constraint is as follows:
P jGkmin ≤P jGk,t ≤P jGkmax ；
wherein, P jGk,t The output power of the jth unit in the multi-energy park at the moment t, P jGkmin Is the minimum power output, P, of the jth unit jGkmax The maximum output power of the jth unit;
the unit climbing restraint is as follows:
wherein, P jGk,t-1 The output power of the jth unit at the moment t-1,
is a time period [ t-1, t]The maximum output power of the inner jth unit which can be adjusted downwards,
is a time period [ t-1, t]The maximum output power of the inner jth unit which can be adjusted upwards.
Further, the multi-energy load balancing constraint is:
wherein L is t The load demand of the multifunctional park at the time t; p t grid For the power transmitted by the grid to the multipotential campus at time t,
the power generation powers of the v-th wind generating set, the m-th photovoltaic generating set and the n-th gas generating set in the multi-energy park at the moment t are respectively.
Further, the carbon emission reduction amount of the multi-energy park before and after the construction of the energy storage system is as follows:
wherein E is 0 And E' is the carbon emission before and after the construction of the energy storage system in the multi-energy park respectively, and consists of the carbon amount transmitted to the multi-energy park by the power grid and the carbon emission of the unit in the multi-energy park;
P G0,t and P G0,t Respectively transmitting active power to the multi-energy park by a power grid at t moments before and after the construction of an energy storage system in the multi-energy park;
and
respectively outputting the output of the nth gas turbine set at the moment t before and after the construction of the energy storage system in the multi-energy park; e.g. of the type G0,t The carbon potential of the power grid at the moment t;
the carbon potential of the nth gas generator set at the moment t; t is an element of [0, T ∈ []And T is a preset period.
In a second aspect, an embodiment of the present invention provides a low-carbon operation optimization apparatus for a multi-energy park, including:
the power grid node carbon potential calculating module is used for calculating the power grid node carbon potential of the multi-energy park according to the prediction curve of the electric quantity and the carbon flow input by the power grid to the multi-energy park;
the state judgment model building module is used for building an energy storage charging and discharging state judgment model according to the carbon potential of the power grid node;
the energy storage system operation construction module is used for constructing an objective function according to the minimum sum of the power generation cost, the carbon emission cost and the energy storage operation cost, and solving various generator set operation curves and energy storage charge-discharge curves in the multi-energy park by combining the energy storage charge-discharge state judgment model and the structural constraint condition;
and the carbon emission reduction calculation module is used for calculating the carbon emission reduction of the multi-energy park before and after the construction of the energy storage system according to the operation curves of the various generator sets and the energy storage charging and discharging curves.
Compared with the prior art, the embodiment of the invention has the following beneficial effects:
calculating the carbon potential of a power grid node of the multi-energy park according to the prediction curves of the electric quantity and the carbon flow input to the multi-energy park by the power grid; constructing an energy storage charging and discharging state judgment model according to the carbon potential of the power grid node; constructing an objective function according to the minimum sum of the power generation cost, the carbon emission cost and the energy storage operation cost, and solving various generator set operation curves and energy storage charge-discharge curves in the multi-energy park by combining an energy storage charge-discharge state judgment model and a structural constraint condition; according to various generator set operation curves and energy storage charge-discharge curves, the carbon emission reduction volume of the multi-energy park before and after the energy storage system is built is calculated, low-carbon operation of the multi-energy park can be optimized, and carbon emission of the multi-energy park is effectively reduced.
Drawings
FIG. 1 is a schematic flow chart of a method for optimizing low-carbon operation in a multi-energy park according to a first embodiment of the present invention;
fig. 2 is a schematic structural diagram of a low-carbon operation optimization device for a multi-energy park according to a second embodiment of the present invention.
Detailed Description
The technical solutions in the present invention will be described clearly and completely with reference to the drawings in the present invention, and it should be apparent that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that, the step numbers in the text are only for convenience of explanation of the specific embodiments, and do not serve to limit the execution sequence of the steps. The method provided by the embodiment can be executed by the relevant terminal device, and the controller is taken as an example to be explained below.
As shown in fig. 1, a first embodiment provides a method for optimizing low-carbon operation in a multi-energy park, which includes steps S1 to S4:
s1, calculating the carbon potential of a power grid node of a multi-energy park according to a prediction curve of electric quantity and carbon flow input by a power grid to the multi-energy park;
s2, constructing an energy storage charging and discharging state judgment model according to the carbon potential of the power grid node;
s3, solving operation curves and energy storage and charge-discharge curves of various generator sets in the multi-energy park by constructing an objective function with the minimum sum of the power generation cost, the carbon emission cost and the energy storage operation cost and combining an energy storage and charge-discharge state judgment model and a structural constraint condition;
and S4, calculating the carbon emission reduction amount of the multi-energy park before and after the construction of the energy storage system according to the operation curves and the energy storage charging and discharging curves of various generator sets.
It should be noted that the multi-energy park is an effective mode for aggregating intermittent low-carbon power, multi-energy coordination control and demand side load control by combining distributed power supplies, energy storage, loads and other related monitoring and protecting devices such as a thermal generator set, a photovoltaic generator set and a gas generator set.
As an example, in step S1, a prediction curve of the electric quantity and the carbon flow input by the power grid to the multi-energy park is obtained, and a power grid node carbon potential of the multi-energy park is calculated according to the prediction curve of the electric quantity and the carbon flow input by the power grid to the multi-energy park.
In step S2, an energy storage charging and discharging state judgment model is constructed according to the carbon potential of the power grid node, and the energy storage charging and discharging state is judged based on the energy storage charging and discharging state judgment model.
In step S3, an objective function is constructed according to the minimum sum of the power generation cost, the carbon emission cost and the energy storage operation cost of the multi-energy park, constraint construction constraint conditions such as line tide constraint, unit operation constraint, multi-energy load balance constraint and the like are considered, and various generator set operation curves and energy storage charging and discharging curves in the multi-energy park are solved according to the objective function, the energy storage charging and discharging state judgment model and the constraint conditions.
In step S4, determining an energy storage charging and discharging state according to various generator set operation curves and energy storage charging and discharging curves, accessing energy storage or adjusting the energy storage charging and discharging state in the multi-energy park to build an energy storage system, calculating the carbon emission reduction amount of the multi-energy park before and after the energy storage system is built, and evaluating the optimization effect of low-carbon operation of the multi-energy park.
The embodiment can introduce carbon emission flow to realize optimizing the low-carbon operation of the multifunctional park, and effectively reduces the carbon emission of the multifunctional park.
In a preferred embodiment, the grid node carbon potential is:
wherein e is k The carbon emission value of the equivalent power generation side caused by the consumption of unit electric quantity of the kth node of the multi-energy park; n is a radical of + The node is a set of all branches into which carbon flows in the branch connected with the kth node; l is the branch number; r l The carbon flow rate of the l branch is,
f is the carbon flow of the first branch, t m Is a unit time; p l The injected power at the kth node for the lth branch.
Illustratively, a prediction curve of the electric quantity and the carbon flow input by the power grid to the multi-energy park is read, and the carbon potential of each node of the power grid is calculated by using the following formula.
The branch carbon flow rate is defined as the carbon flow rate that a single branch passes with the current per unit time. The branch carbon flow rate is calculated by the formula:
in the formula (2), R is the branch carbon flow and has the unit of kgCO 2 H; f is branch carbon flow rate and the unit is kgCO 2 ；t m Is unit time in units of h.
The physical meaning of the carbon potential of the grid node is the carbon emission value which is equivalent to the power generation side and is caused by the consumption of unit electricity at the node. The calculation formula of the carbon potential of the power grid node is as follows:
in the formula (1), e k The carbon emission value on the equivalent power generation side caused by the consumption of unit electric quantity by the kth node of the multi-energy park is given in the unit of kgCO 2 /kWh；N + The set of all branches into which carbon flows in the branch connected with the kth node; l is the branch number; r l The carbon flow rate of the first branch is; p l And the unit of injected power of the kth node for the l branch is kW.
In a preferred embodiment, the energy storage charging and discharging state determination model is:
wherein M is of the order of 10 4 Positive integer of (a), y 1 、y 2 Are all variables of 0/1, and are,
for storing the discharge carbon potential at the time t, t belongs to [0]T is a predetermined period, e k,t The charging carbon potential of the kth node of the multi-energy park at the time t; when y is 1 ＝0、y 2 =1, and
when the stored energy is in a discharge state, when y 1 ＝1、y 2 =0, and
when the stored energy is in a charging state, when y 1 ＝y 2 =0, and
and meanwhile, the stored energy is in an off-grid state.
Illustratively, according to the carbon potential calculation method of the carbon emission flow node, the charging carbon potential e of the kth node of the multi-energy park at the time t can be obtained k,t And obtaining the charging carbon potential of each node at the time t, so as to calculate the discharging carbon potential of the stored energy at the time t, wherein the calculation formula of the discharging carbon potential of the stored energy at the time t is as follows:
in the formula (4), the reaction mixture is,
the discharge carbon potential of the stored energy at the time t is obtained; eta is the charge-discharge efficiency; p i 、e i Respectively the charging power and the charging carbon potential of the node at the moment i; q 0 、F 0 Respectively store energy at T 0 Remaining power and carbon emission flow at that time; and delta T is the time interval length, T is a preset period, and the value is 24, which represents the working time of the energy storage whole day.
Since the formula (1) is nonlinear constraint, the optimization tool box cannot be used for solving, the constraint of the energy storage element is emphasized through the steps (4) - (11), the energy storage boundary condition does not need to be updated repeatedly, the model is converted into a large optimization model, and an optimization solver can be called for direct solving. And the decision is made from the coupling angle of multiple time periods, so that the possible charge and discharge oscillation problem is avoided, and the operation efficiency is improved.
Taking the upper bound of the charged carbon potential of the node at each moment, i.e.
The value of (4) is considered according to the carbon potential curve of the power grid node, and the average value of the minimum value and the maximum value of the carbon potential of the power grid node in the multi-energy park is taken, so that the formula (4) can be changed into the following steps:
by means of a transposition, equation (5) can be converted into:
by converting the product term to a square term, equation (7) can be converted to:
equation (8) satisfies the form of second order cone optimization:
when F' 0 ≥Q 0 And (8) conversion to:
when F' 0 <Q 0 (8) conversion into:
when judging the charging and discharging state of the energy storage element, introducing a 0/1 variable y 1 ，y 2 ：
In formula (11), M is in the order of 10 4 Positive integer of (1), y 1 、y 2 Are all variable quantities of 0/1, and the like,
for storing the discharge carbon potential at time t, t is equal to 0]T is a predetermined period, e i The charging carbon potential of the node at the moment i; when y is 1 ＝0、y 2 =1, and
when the stored energy is in a discharge state, when y 1 ＝1、y 2 =0, and
when the stored energy is in a charging state, when y 1 ＝y 2 =0, and
and meanwhile, the stored energy is in an off-grid state.
In a preferred embodiment, the objective function is:
wherein T is a preset period, T belongs to [0]；
The units of the v-th wind generating set, the m-th photovoltaic generating set and the n-th gas generating set in the multi-energy park respectivelyThe cost of the operation is reduced, and the operation cost is reduced,
the power generation power of the v wind generating set, the m photovoltaic generating set and the n gas generating set at the moment t are respectively; c. C emi In order to be a carbon tax, the carbon tax is,
the carbon potential of the nth gas generator set at the time t, C ESS For unit operating cost of stored energy, P t To store the charging or discharging power at time t.
As an example, assuming a multi-energy park combines a wind generating set (WT), a photovoltaic generating set (PV), a gas generating set (GPP), wherein the gas generating set produces carbon emissions, an objective function is constructed according to the power generation costs of the wind generating set, the photovoltaic generating set, and the gas generating set, and the carbon emission cost of the gas generating set:
in the formula (12), T is a predetermined period, T is in [0]；
The unit operation costs of the v-th wind generating set, the m-th photovoltaic generating set and the n-th gas generating set in the multi-energy park respectively,
the power generation powers of the v-th wind generating set, the m-th photovoltaic generating set and the n-th gas generating set at the moment t are respectively; c. C emi In order to be a carbon tax, the carbon tax is,
carbon of nth gas generator set at t momentPotential, C ESS For unit operating cost of stored energy, P t To store the charging or discharging power at time t.
In a preferred embodiment, the constraints include line flow constraints, unit operation constraints, and multi-energy load balancing constraints.
In a preferred embodiment, the line power flow constraints include line power constraints and node voltage constraints;
line power constraint:
|P l |≤P lmax ，|Q l ≤Q lmax (13)；
wherein, P l Active power of the l branch, P lmax The maximum active power of the l branch; q l For reactive power of the l branch, Q lmax The maximum reactive power of the l branch is obtained;
node voltage constraint:
U k,min ≤U k ≤U k,max (14)；
wherein, U k Voltage at the kth node of the multi-energy park, U k,min Is the minimum voltage of the kth node, U k,max Is the maximum voltage of the kth node.
In a preferred embodiment, the unit operation constraints include a unit power constraint and a unit hill climbing constraint;
and (3) unit power constraint:
P jGkmin ≤P jGk,t ≤P jGkmax (15)；
wherein, P jGk,t The output power, P, of the jth unit in the multi-energy park at the moment of t jGkmin Minimum power output, P, for the jth unit jGkmax The maximum output power of the jth unit;
the unit climbing restriction is:
wherein, P jGk,t-1 For the jth unit at the time of t-1The output power of the power-generating device,
is a time period [ t-1, t]The maximum output power of the inner jth unit which can be adjusted downwards,
is a time period [ t-1, t]The maximum output power of the inner jth unit can be adjusted upwards.
It can be understood that the coal-fired unit comprises a unit power constraint and a unit climbing constraint, and the gas, photovoltaic and wind-power units only have the unit power constraint.
In a preferred embodiment, the multi-energy load balancing constraint is:
wherein L is t The load demand of the multifunctional park at the time t; p t grid For the power transmitted by the grid to the multipotential campus at time t,
the power generation powers of the v-th wind generating set, the m-th photovoltaic generating set and the n-th gas generating set in the multi-energy park at the moment t are respectively.
In a preferred embodiment, the carbon emission reduction of the multi-energy park before and after the construction of the energy storage system is as follows:
wherein E is 0 And E' is the carbon emission before and after the construction of the energy storage system in the multi-energy park respectively, and consists of the carbon amount transmitted to the multi-energy park by the power grid and the carbon emission of the unit in the multi-energy park;
P G0,t and P G0,t ' respectively areThe active power of the multi-energy park transmitted to the multi-energy park by a power grid at the time t before and after the construction of the energy storage system;
and
respectively outputting the output of the nth gas turbine set at the moment t before and after the construction of the energy storage system in the multi-energy park; e.g. of the type G0,t The carbon potential of the power grid at the moment t;
the carbon potential of the nth gas generator set at the time t is shown; t is an element of [0, T ∈ []And T is a preset period.
It can be understood that, according to the node power balance, before and after the multi-energy park is connected to the energy storage, the output of the power grid in each period and the output curve of the gas generator set are different, so that the corresponding carbon emission is different.
Based on the same inventive concept as the first embodiment, the second embodiment provides a multi-energy park low-carbon operation optimization device as shown in fig. 2, which comprises: the power grid node carbon potential calculating module 21 is used for calculating the power grid node carbon potential of the multi-energy park according to the prediction curve of the electric quantity and the carbon flow input by the power grid to the multi-energy park; the state judgment model building module 22 is used for building an energy storage charging and discharging state judgment model according to the carbon potential of the power grid node; the energy storage system operation construction module 23 is used for constructing an objective function according to the minimum sum of the power generation cost, the carbon emission cost and the energy storage operation cost, and solving various generator set operation curves and energy storage charge and discharge curves in the multi-energy park by combining an energy storage charge and discharge state judgment model and a structural constraint condition; and the carbon emission reduction calculation module 24 is used for calculating the carbon emission reduction of the multi-energy park before and after the construction of the energy storage system according to the operation curves and the energy storage charging and discharging curves of various generator sets.
In a preferred embodiment, the grid node carbon potential is:
wherein e is k The carbon emission value on the power generation side is equivalent to that caused by the consumption of unit electric quantity by the kth node of the power grid; n is a radical of + The set of all branches into which carbon flows in the branch connected with the kth node; l is the branch number; r l The carbon flow rate of the l branch is,
f is the carbon flow of the first branch, and t is time; p l The injected power at the kth node for the lth branch.
In a preferred embodiment, the energy storage charging and discharging state determination model is:
wherein M is in the order of 10 4 Positive integer of (1), y 1 、y 2 Are all variable quantities of 0/1, and the like,
for storing the discharge carbon potential at the time t, t is equal to 0]T is a predetermined period, e k,t The charging carbon potential of the kth node of the multi-energy park at the time t; when y is 1 ＝0、y 2 =1, and
when the stored energy is in a discharge state, when y 1 ＝1、y 2 =0, and
when the stored energy is in a charging state, when y 1 ＝y 2 =0, and
and meanwhile, the stored energy is in an off-grid state.
In a preferred embodiment, the objective function is:
wherein T is a preset period, T belongs to [0]；
The unit operation costs of the v-th wind generating set, the m-th photovoltaic generating set and the n-th gas generating set in the multi-energy park respectively,
the power generation powers of the v-th wind generating set, the m-th photovoltaic generating set and the n-th gas generating set at the moment t are respectively; c. C emi In order to be a carbon tax, the carbon tax is,
the carbon potential of the nth gas generator set at the time t, C ESS For unit operating cost of stored energy, P t To store the charging or discharging power at time t.
In a preferred embodiment, the constraints include line flow constraints, unit operation constraints, and multi-energy load balancing constraints.
In a preferred embodiment, the line power flow constraints include line power constraints and node voltage constraints;
line power constraint:
|P l |≤P lmax ，|Q l |≤Q lmax (22)；
wherein, P l Active power of the l branch, P lmax The maximum active power of the l branch; q l For reactive power of the l branch, Q lmax The maximum reactive power of the l branch is obtained;
node voltage constraint:
U k,min ≤U k ≤U k,max (23)；
wherein, U k Voltage at the kth node of the multi-energy park, U k,min Minimum voltage of kth node, U k,max Is the maximum voltage of the kth node.
In a preferred embodiment, the unit operation constraints include a unit power constraint and a unit hill climbing constraint;
and (3) unit power constraint:
P jGkmin ≤P jGk,t ≤P jGkmax (24)；
wherein, P jGk,t The output power of the jth unit in the multi-energy park at the moment t, P jGkmin Minimum power output, P, for the jth unit jGkmax The maximum output power of the jth unit;
the unit climbing restriction is:
wherein, P jGk,t-1 The output power of the jth unit at the moment t-1,
is a time period [ t-1, t]The maximum output power of the inner jth unit which can be adjusted downwards,
is a time period [ t-1, t]The maximum output power of the inner jth unit which can be adjusted upwards.
In a preferred embodiment, the multi-energy load balancing constraint is:
wherein L is t The load demand of the multifunctional park at the time t; p t grid For the power transmitted by the grid to the multipotential campus at time t,
the power generation powers of the v-th wind generating set, the m-th photovoltaic generating set and the n-th gas generating set in the multi-energy park at the moment t are respectively.
In a preferred embodiment, the carbon emission reduction of the multi-energy park before and after the construction of the energy storage system is as follows:
wherein E is 0 And E' is the carbon emission before and after the construction of the energy storage system in the multi-energy park respectively, and consists of the carbon amount transmitted to the multi-energy park by the power grid and the carbon emission of the unit in the multi-energy park;
P G0,t and P G0,t Respectively transmitting active power to the multi-energy park by a power grid at t moments before and after the construction of an energy storage system in the multi-energy park;
and
respectively outputting the output of the nth gas turbine set at the moment t before and after the construction of the energy storage system in the multi-energy park; e.g. of the type G0,t The carbon potential of the power grid at the moment t;
the carbon potential of the nth gas generator set at the time t is shown; t is an element of [0, T ∈ []And T is a preset period.
In summary, the embodiment of the present invention has the following advantages:
calculating the carbon potential of a power grid node of the multi-energy park according to the prediction curves of the electric quantity and the carbon flow input to the multi-energy park by the power grid; constructing an energy storage charging and discharging state judgment model according to the carbon potential of the power grid node; constructing an objective function according to the minimum sum of the power generation cost, the carbon emission cost and the energy storage operation cost, and solving various generator set operation curves and energy storage charge-discharge curves in the multi-energy park by combining an energy storage charge-discharge state judgment model and a structural constraint condition; according to various generator set operation curves and energy storage charge-discharge curves, the carbon emission reduction volume of the multi-energy park before and after the energy storage system is built is calculated, low-carbon operation of the multi-energy park can be optimized, and carbon emission of the multi-energy park is effectively reduced.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
It will be understood by those skilled in the art that all or part of the processes of the above embodiments may be implemented by hardware related to instructions of a computer program, and the computer program may be stored in a computer readable storage medium, and when executed, may include the processes of the above embodiments. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
Claims (10)
1. A low-carbon operation optimization method for a multi-energy park is characterized by comprising the following steps:
calculating the carbon potential of the power grid nodes of the multi-energy park according to the prediction curves of the electric quantity and the carbon flow input by the power grid to the multi-energy park;
constructing an energy storage charging and discharging state judgment model according to the carbon potential of the power grid node;
constructing an objective function according to the minimum sum of the power generation cost, the carbon emission cost and the energy storage operation cost, and solving various generator set operation curves and energy storage charge-discharge curves in the multi-energy park by combining the energy storage charge-discharge state judgment model and the structural constraint condition;
and calculating the carbon emission reduction amount of the multi-energy park before and after the construction of the energy storage system according to the operation curves of the various generator sets and the energy storage charging and discharging curves.
2. The method for optimizing low-carbon operation of a multi-energy park according to claim 1, wherein the grid node carbon potential is:
wherein e is k The carbon emission value of the equivalent power generation side caused by the consumption of unit electric quantity of the kth node of the multi-energy park; n is a radical of + The set of all branches into which carbon flows in the branch connected with the kth node; l is the branch number; r l The carbon flow rate of the l branch is,
f is the carbon flow of the first branch, t m Is a unit time; p l The injected power at the kth node for the lth branch.
3. The method for optimizing the low-carbon operation of the multi-energy park according to claim 1, wherein the energy storage charge-discharge state judgment model is as follows:
wherein M is of the order of 10 4 Positive integer of (1), y 1 、y 2 Are all variables of 0/1, and are,
for storing the discharge carbon potential at the time t, t is equal to 0]T is a predetermined period, e k,t The charging carbon potential of the kth node of the multi-energy park at the time t; when y is 1 ＝0、y 2 =1, and
when the stored energy is in a discharge state, when y 1 ＝1、y 2 =0, and
when the stored energy is in a charging state, when y 1 ＝y 2 =0, and
and meanwhile, the stored energy is in an off-grid state.
4. The method for optimizing low-carbon operation in a multi-energy park according to claim 1, wherein the objective function is:
wherein T is a preset period, T belongs to [0]；
The unit operation costs of the v-th wind generating set, the m-th photovoltaic generating set and the n-th gas generating set in the multi-energy park respectively,
the power generation powers of the v-th wind generating set, the m-th photovoltaic generating set and the n-th gas generating set at the moment t are respectively; c. C emi In order to be a carbon tax, the carbon tax is,
the carbon potential of the nth gas generator set at the time t, C ESS For unit operating cost of stored energy, P t To store the charging or discharging power at time t.
5. The method for optimizing low-carbon operation of a multi-energy park according to claim 1, wherein the constraint conditions include a line flow constraint, a unit operation constraint and a multi-energy load balancing constraint.
6. The method for optimizing low carbon operation in a multi-energy campus of claim 5 wherein said line flow constraints include line power constraints and node voltage constraints;
the line power constraint is:
|P l |≤P lmax ，|Q l |≤Q lmax ；
wherein, P l Active power of the l branch, P lmax The maximum active power of the l branch; q l For reactive power of the l branch, Q lmax The maximum reactive power of the l branch is obtained;
the node voltage constraint is as follows:
U k,min ≤U k ≤U k,max ；
wherein, U k Voltage at the kth node of the multi-energy park, U k,min Is the minimum voltage of the kth node, U k,max Is the maximum voltage of the kth node.
7. The method for optimizing low-carbon operation in a multi-energy park according to claim 5, wherein the unit operation constraints comprise a unit power constraint and a unit hill climbing constraint;
the unit power constraint is as follows:
P jGkmin ≤P jGk,t ≤P jGkmax ；
wherein, P jGk,t The output power of the jth unit in the multi-energy park at the moment t, P jGkmin Is the minimum power output, P, of the jth unit jGkmax The maximum output power of the jth unit;
the unit climbing restraint is as follows:
wherein, P jGk,t-1 The output power of the jth unit at the moment t-1,
is a time period [ t-1, t]The maximum output power of the inner jth unit which can be adjusted downwards,
is a time period [ t-1, t]The maximum output power of the inner jth unit can be adjusted upwards.
8. The method for optimizing low-carbon operation in a multi-energy park according to claim 5, wherein the multi-energy load balancing constraint is:
wherein L is t The load demand of the multifunctional park at the time t; p t grid For the power transmitted by the grid to the multi-energy park at time t,
the power generation powers of the v-th wind generating set, the m-th photovoltaic generating set and the n-th gas generating set in the multi-energy park at the moment t are respectively.
9. The low-carbon operation optimization method for the multi-energy park as claimed in claim 1, wherein the carbon emission reduction amount of the multi-energy park before and after the construction of the energy storage system is as follows:
wherein, E 0 E' is carbon emission of the multi-energy park before and after the energy storage system is built, and consists of carbon quantity transmitted to the multi-energy park by a power grid and unit carbon emission of the multi-energy park;
P G0,t and P G0,t Respectively transmitting active power to the multi-energy park from a power grid of the multi-energy park at t moments before and after the construction of an energy storage system;
and
respectively outputting the output of the nth gas turbine set before and after the construction of the energy storage system in the multi-energy park at the moment t; e.g. of the type G0,t The carbon potential of the power grid at the moment t;
the carbon potential of the nth gas generator set at the time t is shown; t is an element of [0, T ∈ []And T is a preset period.
10. The utility model provides a low carbon operation optimizing apparatus in multipotency garden which characterized in that includes:
the power grid node carbon potential calculating module is used for calculating the power grid node carbon potential of the multi-energy park according to the prediction curve of the electric quantity and the carbon flow input by the power grid to the multi-energy park;
the state judgment model building module is used for building an energy storage charging and discharging state judgment model according to the carbon potential of the power grid node;
the energy storage system operation construction module is used for constructing an objective function according to the minimum sum of the power generation cost, the carbon emission cost and the energy storage operation cost, and solving various generator set operation curves and energy storage charge-discharge curves in the multi-energy park by combining the energy storage charge-discharge state judgment model and the structural constraint condition;
and the carbon emission reduction calculation module is used for calculating the carbon emission reduction of the multi-energy park before and after the construction of the energy storage system according to the operation curves of the various generator sets and the energy storage charging and discharging curves.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211022569.9A CN115392565A (en) | 2022-08-24 | 2022-08-24 | Low-carbon operation optimization method and device for multifunctional park |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211022569.9A CN115392565A (en) | 2022-08-24 | 2022-08-24 | Low-carbon operation optimization method and device for multifunctional park |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115392565A true CN115392565A (en) | 2022-11-25 |
Family
ID=84123008
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211022569.9A Pending CN115392565A (en) | 2022-08-24 | 2022-08-24 | Low-carbon operation optimization method and device for multifunctional park |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115392565A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116896096A (en) * | 2023-06-07 | 2023-10-17 | 国网湖北省电力有限公司经济技术研究院 | Low-carbon optimal operation method and system for power distribution network containing energy storage equipment |
-
2022
- 2022-08-24 CN CN202211022569.9A patent/CN115392565A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116896096A (en) * | 2023-06-07 | 2023-10-17 | 国网湖北省电力有限公司经济技术研究院 | Low-carbon optimal operation method and system for power distribution network containing energy storage equipment |
| CN116896096B (en) * | 2023-06-07 | 2024-03-22 | 国网湖北省电力有限公司经济技术研究院 | Low-carbon optimal operation method and system for power distribution network containing energy storage equipment |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Ma et al. | Optimal allocation of hybrid energy storage systems for smoothing photovoltaic power fluctuations considering the active power curtailment of photovoltaic | |
| CN103986190B (en) | Based on the wind-solar-storage joint electricity generation system smooth control method of generated output curve | |
| Li et al. | Control strategy of battery state of charge for wind/battery hybrid power system | |
| CN109687510B (en) | Uncertainty-considered power distribution network multi-time scale optimization operation method | |
| CN107437824A (en) | A kind of computational methods of the Area distribution formula power supply allowed capacity based on genetic algorithm | |
| CN103956758B (en) | Energy storage SOC optimal control method in a kind of wind storage system | |
| CN104092231A (en) | Method for optimal configuration of independent micro grid mixed energy storage capacity | |
| CN103544656B (en) | A kind of active power distribution network operational control method towards minimum carbon emission | |
| CN106026169B (en) | A kind of composition decomposition optimization method that power distribution network is incorporated to based on more micro-capacitance sensors | |
| CN102856917A (en) | Reactive power optimization method of power distribution network | |
| CN106026113A (en) | Micro-grid system monitoring method having reactive automatic compensation function | |
| CN111563637B (en) | Multi-objective probability optimal power flow calculation method and device based on demand response | |
| CN112132363A (en) | Energy storage site selection and volume fixing method for enhancing system operation robustness | |
| CN106786977A (en) | A kind of charging dispatching method of electric automobile charging station | |
| Wang et al. | An improved min-max power dispatching method for integration of variable renewable energy | |
| CN111884263A (en) | Multi-objective power distribution network optimization method | |
| CN114977320A (en) | Power distribution network source-network charge-storage multi-target collaborative planning method | |
| Baccino et al. | Power and energy control strategies for a vanadium redox flow battery and wind farm combined system | |
| CN115841187A (en) | Method, device, equipment and storage medium for optimizing operation strategy of flexible power distribution network | |
| CN202651806U (en) | Smooth wind-power photovoltaic power generation control system of battery energy storage station | |
| CN115545290A (en) | Distributed energy storage economic optimization configuration method in power distribution network containing photovoltaic power generation | |
| CN115392565A (en) | Low-carbon operation optimization method and device for multifunctional park | |
| Himabindu et al. | Energy management system for grid integrated microgrid using fuzzy logic controller | |
| CN103560533A (en) | Method and system for causing energy storage power station to smooth wind and photovoltaic power generation fluctuation based on change rate | |
| CN110323779B (en) | Method and system for dynamically aggregating power of distributed power generation and energy storage device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |